def main(argv):
"""
:param argv:
"""
directory = util.get_directory_from_command_line(argv, os.path.basename(__file__))
for filename in util.gen_img_paths(directory):
path = os.path.join(directory, filename)
# Open with Bioformats
im = pims.Bioformats(path + '.tif')
# Set scale
micron_per_pixel = im.calibration
# Calculate size range
min_size = max(0.3 / micron_per_pixel, 3)
max_size = min(2.0 / micron_per_pixel, im.frame_shape[0] * 0.5)
im = im[0][:-64]
im = np.flipud(im)
# Locate and refine circles
finder = pf.ParticleFinder(im)
f = finder.locate_particles(size_range=(min_size, max_size))
# Save fit images
plot.save_fits(f, im, path)
# Generate data files and save
report.save_circles_to_csv(f, path, micron_per_pixel)
report.save_circles_to_csv_grouped(f, path, micron_per_pixel)
def main(argv):
"""
:param argv:
"""
directory = util.get_directory_from_command_line(argv, os.path.basename(__file__))
# Open all .tif's in the directory
for filename in util.gen_img_paths(directory):
path = os.path.join(directory, filename)
# Open with Bioformats
im = pims.Bioformats(path + '.tif')
# Set scale
micron_per_pixel = im.calibration
im = im[0][:-64]
im = np.flipud(im)
# Report filename
report_file = os.path.abspath(
os.path.normpath(directory + os.path.sep + 'report' + os.path.sep + filename + '_frame.csv'))
if not os.path.isfile(report_file):
continue
checker = usercheckfits.UserCheckFits(report_file, micron_per_pixel)
f = checker.user_check_fits(im)
# Remove old csv files
summary_file = os.path.abspath(
os.path.normpath(directory + os.path.sep + 'report' + os.path.sep + filename + '_summary.csv'))
if os.path.isfile(summary_file):
os.remove(summary_file)
os.remove(report_file)
# Remove old grouped files
file_path_grouped = os.path.abspath(
os.path.normpath(directory + os.path.sep + 'report' + os.path.sep + re.sub("_\d+$", "", filename)))
report_file_grouped = file_path_grouped + '_grouped_report.csv'
summary_file_grouped = file_path_grouped + '_grouped_summary.csv'
if os.path.isfile(report_file_grouped):
os.remove(report_file_grouped)
if os.path.isfile(summary_file_grouped):
os.remove(summary_file_grouped)
# Remove old fit .tif file
fits_directory = os.path.abspath(os.path.normpath(directory + os.path.sep + 'fits'))
fit_file = os.path.abspath(os.path.normpath(fits_directory + os.path.sep + filename)) + '_fit.tif'
if os.path.isfile(fit_file):
os.remove(fit_file)
# Save fit images
plot.save_fits(f, im, path)
# Generate data files and save
report.save_circles_to_csv(f, path, micron_per_pixel)
report.save_circles_to_csv_grouped(f, path, micron_per_pixel, suffix='_grouped_checked')
